Autonomous exploration is an essential characteristic for Unmanned Aerial Vehicle (UAV) swarms operating in complex and unfamiliar surroundings. This work presents Swarm-RRT*, a unique Guided-Rapidly exploring Random Tree (Guided-RRT*) search algorithm for UAV swarms that enables fast and resilient exploration. The suggested technique combines the swarming paradigm with the conventional RRT approach to improve path planning, coverage, and obstacle avoidance in dynamic situations. A guiding system based on information acquisition and geographical distribution is presented to coordinate the UAV swarm, assuring thorough investigation while reducing duplicate pathways. The Guided-RRT technique dynamically modifies search tree development using the heuristic function and Voronoï principle to guide the exploration while avoiding exploration within the Voronoï region. Extensive simulation tests show that the suggested technique surpasses traditional RRT-based approaches in time complexity, exploration efficiency, coverage rate, and computing cost. This study adds to advances in remote sensing through drone technology by proposing a scalable UAV swarm guided by the RRT* solution for survey, mapping, search and rescue, and environmental monitoring.